DBMS > Apache Impala vs. Apache IoTDB vs. Microsoft Azure Cosmos DB vs. SAP HANA

System Properties Comparison Apache Impala vs. Apache IoTDB vs. Microsoft Azure Cosmos DB vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Globally distributed, horizontally scalable, multi-model database service

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Relational DBMS

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Document store

Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

impala.apache.org

iotdb.apache.org

azure.microsoft.com/services/cosmos-db

www.sap.com/products/hana.html

Technical documentation

impala.apache.org/impala-docs.html

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

learn.microsoft.com/azure/cosmos-db

help.sap.com/hana

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

Microsoft

SAP

Initial release

2013

2018

2014

2010

Current release

4.1.0, June 2022

1.1.0, April 2023

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

Linux

All OS with a Java VM (>= 1.8)

hosted

Appliance or cloud-service

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

JDBC
Native API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Go
Java
Python
Scala

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

JavaScript

SQLScript, R

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

Implicit feature of the cloud service

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

Implicit feature of the cloud service

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Integration with Hadoop and Spark

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Strong Consistency with Raft

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes

Access rights can be defined down to the item level

yes

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